I am a graduate student at Penn State University pursuing Masters of Science in Computer Science and have an interest in the machine learning domain. I have strong technical background with a bachelor's degree in Computer Engineering from India. My key skills include Python, Java, PHP and MySQL. I have worked in the NLP domain and used deep learning models to augment my knowledge. Previously worked in Linux environment and have a six-month industry knowledge as a full stack developer.
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Experience
1. Graduate Teaching Assistant (Part-time) (August 2020 - Present)
Penn State University, University Park, PA, USA
• Promoted as a TA for Fall semester for course Programming and Computation: Data Structures using Python
• Responsibilities include taking recitations, holding office hours and grading assignments
2. Grading Assistant (Part-time) (January 2020 – August 2020)
Penn State University, University Park, PA, USA
• Grading assignments for course CMPSC 132 – Programming and Computations: Data Structures using Python
• Operated with a team of 15 graduate students over spring and summer semesters
3. Software Engineer Intern (LAMP Stack Developer) (January 2019 – May 2019)
Infosys Limited, Mysuru, India
• Developed a Recommendation System using Python for the project that provides food delivery services
• Designed and implemented a designated admin portal to verify the merchants based on business needs
• Worked in Linux environment and used languages PHP, MySQL, HTML, CSS and JavaScript
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Projects
1. Speech Recognition: Detecting Emotions using Human Voice (June 2020 – August 2020)
• Analysed and interpreted multiple research papers for literature review
• Assembled deep learning models using LSTM and CNN to train the dataset to beat the state-of-the-art accuracy
• Converted speech to text for the RAVDESS dataset to improvise the results obtained
• Explored hyperparameter tuning and regularization to check overfitting
2. Text Classification: Toxic Comment Classification (February 2020 – May 2020)
• Administered multi-label classification to categorize comments in 6 categories
• Implemented TF-IDF Vectorizer and Tokenizer class for pre-processing
• Employed machine learning models like Logistic Regression, Ensemble Learning and LSTM Neural Network
3. Recommendation System for Movies – Group Leader (August 2018 – December 2018)
• Incorporated the user-based collaborative filtering technique to build the recommendation system
• Designed and developed using Python, MySQL, HTML and CSS to assist users in better selection of movies
4. Crime Data Analysis and Prediction – Group Member (January 2018 – April 2018)
• Implemented data mining and machine learning algorithms like Naïve-Bayes and K-Means
• Prediction for future crimes done by the Forecast package and visualization by using the Shiny package